The present disclosure relates generally to remaining useful life prediction in prognosis, and particularly to equipment subsystem remaining useful life (RUL) prediction.
Estimating the remaining life of a subsystem is known in the art as prognostics. RUL estimates provide valuable information for operation of modern complex equipment. RUL estimates provide decision making aids that allow operators to change operational characteristics (such as load) which, in turn, may prolong the life of the subsystem. RUL estimates also allow planners to account for upcoming maintenance and set in motion a logistics process that supports a smooth transition from faulted to fully functioning equipment. Predicting remaining life is not straightforward because, ordinarily, remaining life is conditional on future usage conditions, such as load and speed, for example. Examples of equipment that may benefit from the use of remaining life estimates are aircraft engines (both military and commercial), medical equipment, and power plants, for example.
A common approach to prognostics is to employ a model of damage propagation contingent on future use. Such a model is often times based on detailed materials knowledge and makes use of finite element modeling. Because such models are extremely costly to develop, they must be limited to a few important parts of a system, but are rarely applied to subsystems. This approach is often called “lifting”.
Another approach is a data-driven approach to take advantage of time series data where equipment behavior has been tracked via sensor outputs during normal operation all the way to an end of equipment useful life. The end of equipment useful life may represent a totally non-functioning state of the equipment, for example, equipment failure. The end of equipment useful life can also represent a state of the equipment wherein the equipment no longer provides expected results. Alternatively, the end of useful life may be defined as when the equipment reaches a condition of imminent failure. When a reasonably-sized set of these observations exists, pattern recognition algorithms can be employed to recognize these trends and predict remaining life. These predictions are often made under the assumption of near-constant future load conditions. However, such run-to-end-of-equipment-useful-life data are often not available because, when the observed system is complex, expensive, and, safety is important, such as aircraft engines, for example, faults will be repaired before they lead to the end of equipment useful life. This deprives the data driven approach from information necessary for its proper application.
Another approach is a peer-based approach that utilizes information about other equipment to forecast the reliability of equipment within a fleet for the purpose of equipment selection to improve mission reliability. This approach typically focuses on the overall platform, such as a locomotive, or an aircraft, for example, without providing any prognostic insight regarding the components or sub-components of the platform. Furthermore, this approach assumes, in the ideal case, the availability of operational, maintenance, and environmental data for each platform. However, this information may not always be available.
Accordingly, there is a need in the art for a life estimation arrangement that overcomes these drawbacks.